Senior Software Engineer ; MLOps

We’re Aioi R&D Lab - an AI tech hub in one of the fastest-growing insurance companies. We research and develop AI systems that catapult insurance from a slow-moving, traditional past into a data-driven, technology-lead and society-defining future.

We’re looking for dynamic, driven professionals like you to help evolve our business in new directions. You’ll need first-class credentials and a proactive attitude to help us drive the change that underpins our mission.

As a Senior Software Engineer 1 (ML Ops) you’ll be contributing to the design, build, and operation of cloud infrastructure supporting a privacy-preserving AI research programme, working under the direction of the Technical Lead. You will help deliver model hosting and training infrastructure, secure data storage, and agentic development tooling – the foundations the programme’s research tracks depend on.

If you’d like to be part of our brighter future, and share in our success, we’d love to hear from you.

• Contribute to the design, build, and operation of cloud infrastructure supporting a privacy-preserving AI research programme, working under the direction of the Technical Lead.
• Help deliver model hosting and training infrastructure, secure data storage, and agentic development tooling – the foundations the programme’s research tracks depend on.
• Write clean, well-tested, maintainable code and contribute to shared engineering standards, CI/CD pipelines, and documentation.
• Support integration of infrastructure components with partner environments and research workflows, troubleshooting issues as they arise.
• Engage actively with technical trade‑offs, ask good questions, and learn quickly as tool choices and requirements evolve throughout the programme.
• Work within a multi‑partner programme where requirements evolve and final tool choices are not fixed from day one.
• Build robust infrastructure that research teams can depend on.
• Contribute effectively across a broad stack (compute, storage, serving, tooling) rather than specialising in a single area.
• Navigating the balance between moving quickly to support research timelines and maintaining engineering rigor and security standards.
• Grow technical skills and confidence in ML infrastructure through hands‑on work, in an environment that values learning.
• Balance innovation in generative AI with requirements around privacy, data sovereignty, security and operational trust.
• Ensure that project outputs contribute not only to immediate delivery but also to longer‑term reusable capability within the Lab.

• Extensive commercial software experience, with a track record of delivering working, maintainable code in a team setting.
• Solid Python skills, including core data and ML‑adjacent libraries (pandas, numpy, scikit‑learn) and good instincts around code structure, testing and packaging.
• Experience with cloud infrastructure at a practical level: deploying services, managing storage, working with access controls.
• Familiarity with Kubernetes and Helm.
• AWS experience preferred, strong experience with another provider considered.
• Experience with ML infrastructure or data engineering: training pipelines, model serving, experiment tracking, or data pipelines.
• Comfortable with CI/CD pipelines, version control, and containerisation as everyday tools, not just concepts.
• Able to engage with technically complex and ambiguous problems, ask good clarifying questions, research and develop new skills, and work iteratively toward solutions.
• Good communication skills: can explain what they’ve built, justify their decisions, explain what trade‑offs they made, and flag up what they’re unsure about.

• Exposure to LLM serving or fine‑tuning workflows, even at small scale or in personal projects.
• Understanding of data governance or security requirements in regulated industries.
• Interest in privacy‑preserving techniques (differential privacy, secure computation).
• Experience with agentic AI frameworks (Lang Graph, Auto Gen, CrewAI, or similar).
• Familiarity with Kubeflow, KServe, or similar ML orchestration platforms.
• Familiarity with Git Ops / Infrastructure‑as‑code (e.g. Flux,…